“It felt like I was being tailored to the treatment rather than the treatment being tailored to me”: Patient experiences of helpful and unhelpful psychotherapy
Bibliographic record
Abstract
OBJECTIVE: This qualitative study explores patients' experiences of psychotherapy, focusing on elements perceived as helpful or unhelpful and suggestions for improvement in the context of public mental health care. METHODS: A total of 148 adults (Mean age = 32.24, SD = 9.92) who had been or are currently receiving psychological treatment from the National Health Service (NHS) responded to an online survey. The survey included open-ended questions regarding their experiences of psychotherapy, asking them to identify helpful or unhelpful aspects, and suggestions for improvement. Using thematic analysis, key themes were identified. RESULTS: The analysis highlighted the patient's preference for personalized treatment, the importance of therapeutic alliance, the demand for depth in therapy, and life skills and agency as therapeutic outcomes. Participants suggested improvements such as more tailored approaches and stronger therapist-patient relationships, supporting an adaptable, patient-centered model. CONCLUSION: The study highlights challenges in public mental health services where patients might feel their specific needs are not being recognized and met and underscores the importance of personalized treatment plans that satisfy and evolve with patient needs, suggesting that therapists must be attentive and responsive to individual desires to enhance the patient experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".